Add Mask2Former-T low-resolution checkpoint

mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k.py

@@ -0,0 +1,558 @@
+auto_scale_lr = dict(base_batch_size=16, enable=False)
+backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1)
+backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1)
+crop_size = (
+    256,
+    512,
+)
+custom_keys = dict({
+    'absolute_pos_embed':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone':
+    dict(decay_mult=1.0, lr_mult=0.1),
+    'backbone.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.patch_embed.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.0.blocks.0.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.0.blocks.1.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.0.downsample.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.1.blocks.0.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.1.blocks.1.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.1.downsample.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.0.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.1.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.2.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.3.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.4.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.blocks.5.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.2.downsample.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.3.blocks.0.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'backbone.stages.3.blocks.1.norm':
+    dict(decay_mult=0.0, lr_mult=0.1),
+    'level_embed':
+    dict(decay_mult=0.0, lr_mult=1.0),
+    'query_embed':
+    dict(decay_mult=0.0, lr_mult=1.0),
+    'query_feat':
+    dict(decay_mult=0.0, lr_mult=1.0),
+    'relative_position_bias_table':
+    dict(decay_mult=0.0, lr_mult=0.1)
+})
+data_preprocessor = dict(
+    bgr_to_rgb=True,
+    mean=[
+        123.675,
+        116.28,
+        103.53,
+    ],
+    pad_val=0,
+    seg_pad_val=255,
+    size=(
+        256,
+        512,
+    ),
+    std=[
+        58.395,
+        57.12,
+        57.375,
+    ],
+    test_cfg=dict(size_divisor=32),
+    type='SegDataPreProcessor')
+data_root = '/dataset/cityscapes/'
+dataset_type = 'CityscapesDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        by_epoch=False, interval=5000, save_best='mIoU',
+        type='CheckpointHook'),
+    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='SegVisualizationHook'))
+default_scope = 'mmseg'
+depths = [
+    2,
+    2,
+    6,
+    2,
+]
+embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
+env_cfg = dict(
+    cudnn_benchmark=True,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+img_ratios = [
+    0.5,
+    0.75,
+    1.0,
+    1.25,
+    1.5,
+    1.75,
+]
+launcher = 'pytorch'
+load_from = 'work_dirs/mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k_ckpt.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=False)
+model = dict(
+    backbone=dict(
+        attn_drop_rate=0.0,
+        depths=[
+            2,
+            2,
+            6,
+            2,
+        ],
+        drop_path_rate=0.3,
+        drop_rate=0.0,
+        embed_dims=96,
+        frozen_stages=-1,
+        init_cfg=dict(
+            checkpoint=
+            'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth',
+            type='Pretrained'),
+        mlp_ratio=4,
+        num_heads=[
+            3,
+            6,
+            12,
+            24,
+        ],
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        patch_norm=True,
+        qk_scale=None,
+        qkv_bias=True,
+        type='SwinTransformer',
+        window_size=7,
+        with_cp=False),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_val=0,
+        seg_pad_val=255,
+        size=(
+            256,
+            512,
+        ),
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        test_cfg=dict(size_divisor=32),
+        type='SegDataPreProcessor'),
+    decode_head=dict(
+        align_corners=False,
+        enforce_decoder_input_project=False,
+        feat_channels=256,
+        in_channels=[
+            96,
+            192,
+            384,
+            768,
+        ],
+        loss_cls=dict(
+            class_weight=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                0.1,
+            ],
+            loss_weight=2.0,
+            reduction='mean',
+            type='mmdet.CrossEntropyLoss',
+            use_sigmoid=False),
+        loss_dice=dict(
+            activate=True,
+            eps=1.0,
+            loss_weight=5.0,
+            naive_dice=True,
+            reduction='mean',
+            type='mmdet.DiceLoss',
+            use_sigmoid=True),
+        loss_mask=dict(
+            loss_weight=5.0,
+            reduction='mean',
+            type='mmdet.CrossEntropyLoss',
+            use_sigmoid=True),
+        num_classes=19,
+        num_queries=100,
+        num_transformer_feat_level=3,
+        out_channels=256,
+        pixel_decoder=dict(
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                init_cfg=None,
+                layer_cfg=dict(
+                    ffn_cfg=dict(
+                        act_cfg=dict(inplace=True, type='ReLU'),
+                        embed_dims=256,
+                        feedforward_channels=1024,
+                        ffn_drop=0.0,
+                        num_fcs=2),
+                    self_attn_cfg=dict(
+                        batch_first=True,
+                        dropout=0.0,
+                        embed_dims=256,
+                        im2col_step=64,
+                        init_cfg=None,
+                        norm_cfg=None,
+                        num_heads=8,
+                        num_levels=3,
+                        num_points=4)),
+                num_layers=6),
+            init_cfg=None,
+            norm_cfg=dict(num_groups=32, type='GN'),
+            num_outs=3,
+            positional_encoding=dict(normalize=True, num_feats=128),
+            type='mmdet.MSDeformAttnPixelDecoder'),
+        positional_encoding=dict(normalize=True, num_feats=128),
+        strides=[
+            4,
+            8,
+            16,
+            32,
+        ],
+        train_cfg=dict(
+            assigner=dict(
+                match_costs=[
+                    dict(type='mmdet.ClassificationCost', weight=2.0),
+                    dict(
+                        type='mmdet.CrossEntropyLossCost',
+                        use_sigmoid=True,
+                        weight=5.0),
+                    dict(
+                        eps=1.0,
+                        pred_act=True,
+                        type='mmdet.DiceCost',
+                        weight=5.0),
+                ],
+                type='mmdet.HungarianAssigner'),
+            importance_sample_ratio=0.75,
+            num_points=12544,
+            oversample_ratio=3.0,
+            sampler=dict(type='mmdet.MaskPseudoSampler')),
+        transformer_decoder=dict(
+            init_cfg=None,
+            layer_cfg=dict(
+                cross_attn_cfg=dict(
+                    attn_drop=0.0,
+                    batch_first=True,
+                    dropout_layer=None,
+                    embed_dims=256,
+                    num_heads=8,
+                    proj_drop=0.0),
+                ffn_cfg=dict(
+                    act_cfg=dict(inplace=True, type='ReLU'),
+                    add_identity=True,
+                    dropout_layer=None,
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    ffn_drop=0.0,
+                    num_fcs=2),
+                self_attn_cfg=dict(
+                    attn_drop=0.0,
+                    batch_first=True,
+                    dropout_layer=None,
+                    embed_dims=256,
+                    num_heads=8,
+                    proj_drop=0.0)),
+            num_layers=9,
+            return_intermediate=True),
+        type='Mask2FormerHead'),
+    test_cfg=dict(mode='whole'),
+    train_cfg=dict(),
+    type='EncoderDecoder')
+num_classes = 19
+optim_wrapper = dict(
+    clip_grad=dict(max_norm=0.01, norm_type=2),
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        eps=1e-08,
+        lr=0.0001,
+        type='AdamW',
+        weight_decay=0.05),
+    paramwise_cfg=dict(
+        custom_keys=dict({
+            'absolute_pos_embed':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone':
+            dict(decay_mult=1.0, lr_mult=0.1),
+            'backbone.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.patch_embed.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.0.blocks.0.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.0.blocks.1.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.0.downsample.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.1.blocks.0.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.1.blocks.1.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.1.downsample.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.0.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.1.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.2.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.3.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.4.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.blocks.5.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.2.downsample.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.3.blocks.0.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'backbone.stages.3.blocks.1.norm':
+            dict(decay_mult=0.0, lr_mult=0.1),
+            'level_embed':
+            dict(decay_mult=0.0, lr_mult=1.0),
+            'query_embed':
+            dict(decay_mult=0.0, lr_mult=1.0),
+            'query_feat':
+            dict(decay_mult=0.0, lr_mult=1.0),
+            'relative_position_bias_table':
+            dict(decay_mult=0.0, lr_mult=0.1)
+        }),
+        norm_decay_mult=0.0),
+    type='OptimWrapper')
+optimizer = dict(
+    betas=(
+        0.9,
+        0.999,
+    ),
+    eps=1e-08,
+    lr=0.0001,
+    type='AdamW',
+    weight_decay=0.05)
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=False,
+        end=90000,
+        eta_min=0,
+        power=0.9,
+        type='PolyLR'),
+]
+pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth'
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
+        data_root='/dataset/cityscapes/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                2048,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='CityscapesDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        2048,
+        1024,
+    ), type='Resize'),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs'),
+]
+train_cfg = dict(max_iters=90000, type='IterBasedTrainLoop', val_interval=5000)
+train_dataloader = dict(
+    batch_size=2,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='leftImg8bit/train', seg_map_path='gtFine/train'),
+        data_root='/dataset/cityscapes/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations'),
+            dict(
+                max_size=4096,
+                resize_type='ResizeShortestEdge',
+                scales=[
+                    512,
+                    614,
+                    716,
+                    819,
+                    921,
+                    1024,
+                    1126,
+                    1228,
+                    1331,
+                    1433,
+                    1536,
+                    1638,
+                    1740,
+                    1843,
+                    1945,
+                    2048,
+                ],
+                type='RandomChoiceResize'),
+            dict(
+                cat_max_ratio=0.75, crop_size=(
+                    256,
+                    512,
+                ), type='RandomCrop'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PhotoMetricDistortion'),
+            dict(type='PackSegInputs'),
+        ],
+        type='CityscapesDataset'),
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='InfiniteSampler'))
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        max_size=4096,
+        resize_type='ResizeShortestEdge',
+        scales=[
+            512,
+            614,
+            716,
+            819,
+            921,
+            1024,
+            1126,
+            1228,
+            1331,
+            1433,
+            1536,
+            1638,
+            1740,
+            1843,
+            1945,
+            2048,
+        ],
+        type='RandomChoiceResize'),
+    dict(cat_max_ratio=0.75, crop_size=(
+        256,
+        512,
+    ), type='RandomCrop'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs'),
+]
+tta_model = dict(type='SegTTAModel')
+tta_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(
+        transforms=[
+            [
+                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
+                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
+            ],
+            [
+                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
+                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
+            ],
+            [
+                dict(type='LoadAnnotations'),
+            ],
+            [
+                dict(type='PackSegInputs'),
+            ],
+        ],
+        type='TestTimeAug'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        data_prefix=dict(
+            img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
+        data_root='/dataset/cityscapes/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                2048,
+                1024,
+            ), type='Resize'),
+            dict(type='LoadAnnotations'),
+            dict(type='PackSegInputs'),
+        ],
+        type='CityscapesDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    iou_metrics=[
+        'mIoU',
+    ], type='IoUMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='SegLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = './work_dirs/mask2former-swint-8xb2-512x1024-90k'

mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k_ckpt.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d1f9120df9c9046743ecb3b2b8162588563544c9c27d74761fd3751c38e8d86
+size 622958776